Shape completion enabled robotic grasping
Jacob Varley,Chad DeChant,Adam Richardson,Joaquín Ruales,Peter K. Allen +4 more
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TLDR
This work provides an architecture to enable robotic grasp planning via shape completion through the use of a 3D convolutional neural network trained on a new open source dataset of over 440,000 3D exemplars captured from varying viewpoints.Abstract:
This work provides an architecture to enable robotic grasp planning via shape completion. Shape completion is accomplished through the use of a 3D convolutional neural network (CNN). The network is trained on our own new open source dataset of over 440,000 3D exemplars captured from varying viewpoints. At runtime, a 2.5D pointcloud captured from a single point of view is fed into the CNN, which fills in the occluded regions of the scene, allowing grasps to be planned and executed on the completed object. Runtime shape completion is very rapid because most of the computational costs of shape completion are borne during offline training. We explore how the quality of completions vary based on several factors. These include whether or not the object being completed existed in the training data and how many object models were used to train the network. We also look at the ability of the network to generalize to novel objects allowing the system to complete previously unseen objects at runtime. Finally, experimentation is done both in simulation and on actual robotic hardware to explore the relationship between completion quality and the utility of the completed mesh model for grasping.read more
Citations
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Proceedings ArticleDOI
Semantic Scene Completion from a Single Depth Image
TL;DR: The semantic scene completion network (SSCNet) is introduced, an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum.
Posted Content
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
Jeffrey Mahler,Jacky Liang,Sherdil Niyaz,Michael Laskey,Richard Doan,Xinyu Liu,Juan Aparicio Ojea,Ken Goldberg +7 more
TL;DR: In this article, a grasp quality convolutional neural network (GQ-CNN) is trained from a synthetic dataset of 6.7 million point clouds, grasps and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in randomized poses on a table.
Proceedings ArticleDOI
PCN: Point Completion Network
TL;DR: Point Completion Network (PCN) as discussed by the authors directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation about the underlying shape, which enables the generation of fine-grained completions while maintaining a small number of parameters.
Proceedings ArticleDOI
TopNet: Structural Point Cloud Decoder
TL;DR: This work proposes a novel decoder that generates a structured point cloud without assuming any specific structure or topology on the underlying point set, and significantly outperforms state-of-the-art 3D point cloud completion methods on the Shapenet dataset.
Proceedings ArticleDOI
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
TL;DR: This work proposes a data-driven method for recovering missing parts of 3D shapes based on a new deep learning architecture consisting of a global structure inference network and a local geometry refinement network that outperforms existing state-of-the-art work on shape completion.
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